For answering the first research question, we evaluate whether RealiT  improves the single token bug localization
and repair performance by training with real bug fixes.
Since we are interested in the impact of real bug fixes on the training process, we compare our results with several baseline algorithms trained purely on mutants. For the comparison, we consider bug localization and repair models
based on recursive neural networks (RNN) [1]}, transformers (absolute positions) [2]}, graph neural networks (GNNOur evaluation setup differs slightly from [3]} in that only individual function implementations are considered. Therefore, graph level information that would require access to the implementation context cannot be computed.) [3]} and GREAT [2]}.
All baseline models are trained in a supervised setting purely on mutants. The training dataset is constructed similar to the pre-training dataset used for RealiT  (with \(k\)  = 5 mutants injected).
The baselines are trained for 300 epochs (a 200k examples per epoch) with early-stopping on our validation set.
For RealiT, we skip the last epoch and instead fine-tune on real bug fixes.
